From c55a7de0b064b7f3e21584082c93120aef13e480 Mon Sep 17 00:00:00 2001
From: Lewis Woolfson <lewis.woolfson@paconsulting.com>
Date: Wed, 27 May 2020 18:11:08 +0100
Subject: [PATCH] added model sets and params

---
 modelling/scheduler.py | 52 +++++++++++++++++++++++++++++++++++-------
 1 file changed, 44 insertions(+), 8 deletions(-)

diff --git a/modelling/scheduler.py b/modelling/scheduler.py
index 8349717..55f68b6 100644
--- a/modelling/scheduler.py
+++ b/modelling/scheduler.py
@@ -3,25 +3,55 @@ import numpy as np
 import pyomo.environ as pe
 import pyomo.gdp as pyogdp
 from pyomo.core.base.set_types import Any
+import os
 
 
 # check with Taha if code is too similar to Alstom?
 class TheatreScheduler:
 
-    def __init__(self):
-        pass
+    def __init__(self, case_file_path, session_file_path):
+        """
+        Read case and session data into Pandas DataFrames
+        Args:
+            case_file_path (str): path to case data in CSV format
+            session_file_path (str): path to theatre session data in CSV format
+        """
+        try:
+            self.df_cases = pd.read_csv(case_file_path)
+        except FileNotFoundError:
+            print("Case data not found.")
 
-    def _generate_cases(self):
-        pass
+        try:
+            self.df_sessions = pd.read_csv(session_file_path)
+        except FileNotFoundError:
+            print("Session data not found")
+
+    def _generate_case_durations(self):
+        """
+        Generate mapping of cases IDs to median case time for the procedure
+        Returns:
+            (dict): dictionary with CaseID as key and median case time (mins) for procedure as value
+        """
+        return pd.Series(self.df_cases["Median Duration"].values, index=self.df_cases["CaseID"]).to_dict()
+
+    def _generate_session_durations(self):
+        """
+        Generate mapping of all theatre sessions IDs to session duration in minutes
+        Returns:
+            (dict): dictionary with SessionID as key and session duration as value
+        """
+        return pd.Series(self.df_sessions["Duration"].values, index=self.df_sessions["SessionID"]).to_dict()
 
     def create_model(self):
         model = pe.ConcreteModel()
 
         # Model Data
-        model.CASES = pe.Set()  # indexed by (caseID, sessionID)
-        model.CASE_DURATIONS = pe.Param()  # median case times
-        model.SESSION_DURATIONS = pe.param()  # session durations
-        model.M = pe.Param()  # big M
+        model.CASES = pe.Set(initialize=self.df_cases["CaseID"].tolist())
+        model.SESSIONS = pe.Set(initialize=self.df_sessions["SessionID"].tolist())
+        model.TASKS = pe.Set(initialize=model.CASES * model.SESSIONS, dimen=2)
+        model.CASE_DURATIONS = pe.Param(model.CASES, initialize=self._generate_case_durations())
+        model.SESSION_DURATIONS = pe.param(model.SESSIONS, initalize=self._generate_session_durations())
+        model.M = pe.Param(initialize=1e7)  # big M
         max_util = 0.85
 
         # Decision Variables
@@ -62,3 +92,9 @@ class TheatreScheduler:
 
     def solve(self):
         pass
+
+
+if __name__ == "__main__":
+    case_path = os.path.join(os.path.dirname(os.getcwd()), "data", "case_data.csv")
+    session_path = os.path.join(os.path.dirname(os.getcwd()), "data", "session_data.csv")
+    scheduler = TheatreScheduler(case_file_path=case_path, session_file_path=session_path)